Abstract
In this paper, we develop a terrain attribute recognition system for CPG-based legged robots. First, a low-cost sensing hardware device is designed to be integrated into the robot, including a tactile sensor array and RGB camera. Second, for the tactile modality, a novel terrain attribute recognition framework is proposed. A data generation strategy that adapts to the motion characteristics is presented, which transforms the original tactile signal into a structured representation, and extract meaningful features. Based on unsupervised and supervised machine learning classifiers, the recognition rates reach 94.0% and 95.5%, and the switching time is 1 to 3 steps. Third, for the recognition of terrain attributes in the visual modality, a lightweight real-time mobile attention coding network (MACNet) is proposed as an end-to-end model, which shows an exhibiting an accuracy of 88.5% on the improved GTOS mobile data set, 169FPS inference speed and 6.6 MB model parameter occupancy. Finally, these two methods are simultaneously applied to the AmphiHex-II robot for outdoor experiments. Experimental results show that each modality has its own advantages and disadvantages, and the complementary relationship between multiple modalities plays an irreplaceable role in a broader scene.
Video of this work: https://v.youku.com/v_show/id_XNTE3NTM0NjE1Mg==.html.
H. Chen and X. Zhu—contribute equally to this work.
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Chen, H., Zhu, X., Zhu, S., Chen, H., Zhang, S., Lou, Y. (2021). Terrain Attribute Recognition System for CPG-Based Legged Robot. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13015. Springer, Cham. https://doi.org/10.1007/978-3-030-89134-3_51
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